Subclass for random search tuning.

The random points are sampled by paradox::generate_design_random().

Source

Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281--305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.

Dictionary

This Tuner can be instantiated via the dictionary mlr_tuners or with the associated sugar function tnr():

TunerRandomSearch$new()
mlr_tuners$get("random_search")
tnr("random_search")

Parallelization

In order to support general termination criteria and parallelization, we evaluate points in a batch-fashion of size batch_size. Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria. A batch contains of batch_size times resampling$iters jobs. E.g., if you set a batch size of 10 points and do a 5-fold cross validation, you can utilize up to 50 cores.

Parallelization is supported via package future (see mlr3::benchmark()'s section on parallelization for more details).

Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Parameters

batch_size

integer(1)
Maximum number of points to try in a batch.

Progress Bars

$optimize() supports progress bars via the package progressr combined with a Terminator. Simply wrap the function in progressr::with_progress() to enable them. We recommend to use package progress as backend; enable with progressr::handlers("progress").

See also

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerRandomSearch

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

TunerRandomSearch$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

TunerRandomSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# retrieve task task = tsk("pima") # load learner and set search space learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)) # hyperparameter tuning on the pima indians diabetes data set instance = tune( method = "random_search", task = task, learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), term_evals = 10 ) # best performing hyperparameter configuration instance$result
#> cp learner_param_vals x_domain classif.ce #> 1: -4.523676 <list[2]> <list[1]> 0.2265625
# all evaluated hyperparameter configuration as.data.table(instance$archive)
#> cp classif.ce x_domain_cp runtime_learners timestamp #> 1: -6.410471 0.2773438 0.0016442491 0.012 2021-09-16 04:23:26 #> 2: -5.269410 0.2656250 0.0051466475 0.012 2021-09-16 04:23:26 #> 3: -7.059103 0.2773438 0.0008595488 0.012 2021-09-16 04:23:26 #> 4: -3.730501 0.2421875 0.0239808065 0.010 2021-09-16 04:23:26 #> 5: -6.727419 0.2773438 0.0011976204 0.012 2021-09-16 04:23:27 #> 6: -6.256072 0.2773438 0.0019187674 0.010 2021-09-16 04:23:27 #> 7: -5.754732 0.2656250 0.0031677556 0.012 2021-09-16 04:23:27 #> 8: -7.431878 0.2773438 0.0005920747 0.011 2021-09-16 04:23:27 #> 9: -4.523676 0.2265625 0.0108490725 0.011 2021-09-16 04:23:27 #> 10: -4.350720 0.2265625 0.0128975237 0.012 2021-09-16 04:23:27 #> batch_nr resample_result #> 1: 1 <ResampleResult[20]> #> 2: 2 <ResampleResult[20]> #> 3: 3 <ResampleResult[20]> #> 4: 4 <ResampleResult[20]> #> 5: 5 <ResampleResult[20]> #> 6: 6 <ResampleResult[20]> #> 7: 7 <ResampleResult[20]> #> 8: 8 <ResampleResult[20]> #> 9: 9 <ResampleResult[20]> #> 10: 10 <ResampleResult[20]>
# fit final model on complete data set learner$param_set$values = instance$result_learner_param_vals learner$train(task)